Algorithm unfolding (or unrolling) [1]}, [2]}, [3]}, [4]} refers to the general notion of selecting an iterative model-based approach to solve a problem of interest and building a problem-specific neural network with layers inspired by these iterations.
Backed-up by its recent success on several problems, we present here the first unfolded algorithm for power allocation in wireless networks.
Notice, however, that the action of unfolding is not uniquely determined.
Several decisions must be made with the objective of incorporating domain knowledge as well as striking the right balance between interpretability and performance.
These decisions include selecting what parameters become trainable and what trainable architectures are used to represent these parameters.
In this paper, we develop and evaluate a way to unfold WMMSE using graph neural networks.
